Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations331267
Missing cells2210825
Missing cells (%)35.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory48.0 MiB
Average record size in memory152.0 B

Variable types

Text2
Categorical3
Numeric13
Boolean1

Alerts

MRG has constant value "False" Constant
ARPU_SEGMENT is highly overall correlated with FREQUENCE and 7 other fieldsHigh correlation
CHURN is highly overall correlated with REGULARITYHigh correlation
FREQUENCE is highly overall correlated with ARPU_SEGMENT and 6 other fieldsHigh correlation
FREQUENCE_RECH is highly overall correlated with ARPU_SEGMENT and 6 other fieldsHigh correlation
FREQ_TOP_PACK is highly overall correlated with ARPU_SEGMENT and 6 other fieldsHigh correlation
MONTANT is highly overall correlated with ARPU_SEGMENT and 7 other fieldsHigh correlation
ON_NET is highly overall correlated with ARPU_SEGMENT and 4 other fieldsHigh correlation
ORANGE is highly overall correlated with ARPU_SEGMENT and 6 other fieldsHigh correlation
REGULARITY is highly overall correlated with ARPU_SEGMENT and 7 other fieldsHigh correlation
REVENUE is highly overall correlated with ARPU_SEGMENT and 7 other fieldsHigh correlation
TENURE is highly imbalanced (86.4%) Imbalance
REGION has 130539 (39.4%) missing values Missing
MONTANT has 116364 (35.1%) missing values Missing
FREQUENCE_RECH has 116364 (35.1%) missing values Missing
REVENUE has 111611 (33.7%) missing values Missing
ARPU_SEGMENT has 111611 (33.7%) missing values Missing
FREQUENCE has 111611 (33.7%) missing values Missing
DATA_VOLUME has 163104 (49.2%) missing values Missing
ON_NET has 121131 (36.6%) missing values Missing
ORANGE has 137819 (41.6%) missing values Missing
TIGO has 198144 (59.8%) missing values Missing
ZONE1 has 305097 (92.1%) missing values Missing
ZONE2 has 310242 (93.7%) missing values Missing
TOP_PACK has 138593 (41.8%) missing values Missing
FREQ_TOP_PACK has 138594 (41.8%) missing values Missing
DATA_VOLUME is highly skewed (γ1 = 28.11494861) Skewed
user_id has unique values Unique
DATA_VOLUME has 49051 (14.8%) zeros Zeros
ON_NET has 16552 (5.0%) zeros Zeros
ORANGE has 9496 (2.9%) zeros Zeros
TIGO has 14458 (4.4%) zeros Zeros
ZONE1 has 9212 (2.8%) zeros Zeros
ZONE2 has 6217 (1.9%) zeros Zeros

Reproduction

Analysis started2025-04-28 17:37:06.158209
Analysis finished2025-04-28 17:38:03.459185
Duration57.3 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

user_id
Text

Unique 

Distinct331267
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
2025-04-28T17:38:04.001582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length40
Mean length40
Min length40

Characters and Unicode

Total characters13250680
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique331267 ?
Unique (%)100.0%

Sample

1st row00000bfd7d50f01092811bc0c8d7b0d6fe7c3596
2nd row00000cb4a5d760de88fecb38e2f71b7bec52e834
3rd row00001654a9d9f96303d9969d0a4a851714a4bb57
4th row00001dd6fa45f7ba044bd5d84937be464ce78ac2
5th row000028d9e13a595abe061f9b58f3d76ab907850f
ValueCountFrequency (%)
00001dd6fa45f7ba044bd5d84937be464ce78ac2 1
 
< 0.1%
276e99744062bb493e52ef26a09e1dfc5446cea6 1
 
< 0.1%
00000bfd7d50f01092811bc0c8d7b0d6fe7c3596 1
 
< 0.1%
276e50b83a7dc315a086963feb70ba55751399de 1
 
< 0.1%
276e516c979a6afa607b9cbd7eab62216652f44b 1
 
< 0.1%
276e53f7498551fff1234d9e709889c84405ba78 1
 
< 0.1%
276e56504fe3751cd86222993b4a457a5058e043 1
 
< 0.1%
276e59a531eba872a57d29ea94b225fc898a8a61 1
 
< 0.1%
276e5a3c538792af173d795fe57f3331bc3d52a6 1
 
< 0.1%
276e5b3ea0f146740ea1df8a4c349c14d9e17f69 1
 
< 0.1%
Other values (331257) 331257
> 99.9%
2025-04-28T17:38:04.828690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 946513
 
7.1%
1 945963
 
7.1%
2 876025
 
6.6%
3 812771
 
6.1%
6 812255
 
6.1%
5 811817
 
6.1%
4 810901
 
6.1%
7 807111
 
6.1%
8 804606
 
6.1%
b 804423
 
6.1%
Other values (6) 4818295
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13250680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 946513
 
7.1%
1 945963
 
7.1%
2 876025
 
6.6%
3 812771
 
6.1%
6 812255
 
6.1%
5 811817
 
6.1%
4 810901
 
6.1%
7 807111
 
6.1%
8 804606
 
6.1%
b 804423
 
6.1%
Other values (6) 4818295
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13250680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 946513
 
7.1%
1 945963
 
7.1%
2 876025
 
6.6%
3 812771
 
6.1%
6 812255
 
6.1%
5 811817
 
6.1%
4 810901
 
6.1%
7 807111
 
6.1%
8 804606
 
6.1%
b 804423
 
6.1%
Other values (6) 4818295
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13250680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 946513
 
7.1%
1 945963
 
7.1%
2 876025
 
6.6%
3 812771
 
6.1%
6 812255
 
6.1%
5 811817
 
6.1%
4 810901
 
6.1%
7 807111
 
6.1%
8 804606
 
6.1%
b 804423
 
6.1%
Other values (6) 4818295
36.4%

REGION
Categorical

Missing 

Distinct14
Distinct (%)< 0.1%
Missing130539
Missing (%)39.4%
Memory size2.5 MiB
DAKAR
78959 
THIES
27749 
SAINT-LOUIS
18394 
LOUGA
15244 
KAOLACK
14901 
Other values (9)
45481 

Length

Max length11
Median length5
Mean length6.3245188
Min length5

Characters and Unicode

Total characters1269508
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFATICK
2nd rowDAKAR
3rd rowDAKAR
4th rowLOUGA
5th rowLOUGA

Common Values

ValueCountFrequency (%)
DAKAR 78959
23.8%
THIES 27749
 
8.4%
SAINT-LOUIS 18394
 
5.6%
LOUGA 15244
 
4.6%
KAOLACK 14901
 
4.5%
DIOURBEL 10306
 
3.1%
TAMBACOUNDA 8440
 
2.5%
KAFFRINE 6790
 
2.0%
KOLDA 5879
 
1.8%
FATICK 5688
 
1.7%
Other values (4) 8378
 
2.5%
(Missing) 130539
39.4%

Length

2025-04-28T17:38:05.048757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dakar 78959
39.3%
thies 27749
 
13.8%
saint-louis 18394
 
9.2%
louga 15244
 
7.6%
kaolack 14901
 
7.4%
diourbel 10306
 
5.1%
tambacounda 8440
 
4.2%
kaffrine 6790
 
3.4%
kolda 5879
 
2.9%
fatick 5688
 
2.8%
Other values (4) 8378
 
4.2%

Most occurring characters

ValueCountFrequency (%)
A 273845
21.6%
K 127277
10.0%
D 104230
 
8.2%
R 99382
 
7.8%
I 94462
 
7.4%
O 77296
 
6.1%
S 65024
 
5.1%
L 64724
 
5.1%
T 64676
 
5.1%
U 56516
 
4.5%
Other values (10) 242076
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1269508
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 273845
21.6%
K 127277
10.0%
D 104230
 
8.2%
R 99382
 
7.8%
I 94462
 
7.4%
O 77296
 
6.1%
S 65024
 
5.1%
L 64724
 
5.1%
T 64676
 
5.1%
U 56516
 
4.5%
Other values (10) 242076
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1269508
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 273845
21.6%
K 127277
10.0%
D 104230
 
8.2%
R 99382
 
7.8%
I 94462
 
7.4%
O 77296
 
6.1%
S 65024
 
5.1%
L 64724
 
5.1%
T 64676
 
5.1%
U 56516
 
4.5%
Other values (10) 242076
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1269508
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 273845
21.6%
K 127277
10.0%
D 104230
 
8.2%
R 99382
 
7.8%
I 94462
 
7.4%
O 77296
 
6.1%
S 65024
 
5.1%
L 64724
 
5.1%
T 64676
 
5.1%
U 56516
 
4.5%
Other values (10) 242076
19.1%

TENURE
Categorical

Imbalance 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
K > 24 month
314207 
I 18-21 month
 
7009
H 15-18 month
 
3980
G 12-15 month
 
2313
J 21-24 month
 
1944
Other values (3)
 
1814

Length

Max length13
Median length12
Mean length12.044746
Min length11

Characters and Unicode

Total characters3990027
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowK > 24 month
2nd rowI 18-21 month
3rd rowK > 24 month
4th rowK > 24 month
5th rowK > 24 month

Common Values

ValueCountFrequency (%)
K > 24 month 314207
94.9%
I 18-21 month 7009
 
2.1%
H 15-18 month 3980
 
1.2%
G 12-15 month 2313
 
0.7%
J 21-24 month 1944
 
0.6%
F 9-12 month 1391
 
0.4%
E 6-9 month 287
 
0.1%
D 3-6 month 136
 
< 0.1%

Length

2025-04-28T17:38:05.237351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-28T17:38:05.422524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
month 331267
25.3%
k 314207
24.0%
314207
24.0%
24 314207
24.0%
i 7009
 
0.5%
18-21 7009
 
0.5%
h 3980
 
0.3%
15-18 3980
 
0.3%
g 2313
 
0.2%
12-15 2313
 
0.2%
Other values (8) 7516
 
0.6%

Most occurring characters

ValueCountFrequency (%)
976741
24.5%
n 331267
 
8.3%
o 331267
 
8.3%
m 331267
 
8.3%
h 331267
 
8.3%
t 331267
 
8.3%
2 328808
 
8.2%
4 316151
 
7.9%
K 314207
 
7.9%
> 314207
 
7.9%
Other values (14) 83578
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3990027
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
976741
24.5%
n 331267
 
8.3%
o 331267
 
8.3%
m 331267
 
8.3%
h 331267
 
8.3%
t 331267
 
8.3%
2 328808
 
8.2%
4 316151
 
7.9%
K 314207
 
7.9%
> 314207
 
7.9%
Other values (14) 83578
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3990027
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
976741
24.5%
n 331267
 
8.3%
o 331267
 
8.3%
m 331267
 
8.3%
h 331267
 
8.3%
t 331267
 
8.3%
2 328808
 
8.2%
4 316151
 
7.9%
K 314207
 
7.9%
> 314207
 
7.9%
Other values (14) 83578
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3990027
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
976741
24.5%
n 331267
 
8.3%
o 331267
 
8.3%
m 331267
 
8.3%
h 331267
 
8.3%
t 331267
 
8.3%
2 328808
 
8.2%
4 316151
 
7.9%
K 314207
 
7.9%
> 314207
 
7.9%
Other values (14) 83578
 
2.1%

MONTANT
Real number (ℝ)

High correlation  Missing 

Distinct2194
Distinct (%)1.0%
Missing116364
Missing (%)35.1%
Infinite0
Infinite (%)0.0%
Mean5554.3982
Minimum10
Maximum290500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-04-28T17:38:05.636241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile250
Q11000
median3000
Q37400
95-th percentile18530.6
Maximum290500
Range290490
Interquartile range (IQR)6400

Descriptive statistics

Standard deviation7138.7462
Coefficient of variation (CV)1.285242
Kurtosis46.840777
Mean5554.3982
Median Absolute Deviation (MAD)2400
Skewness4.0943968
Sum1.1936568 × 109
Variance50961697
MonotonicityNot monotonic
2025-04-28T17:38:05.859736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 17342
 
5.2%
1000 12758
 
3.9%
1500 7417
 
2.2%
2000 7137
 
2.2%
200 6198
 
1.9%
3000 5392
 
1.6%
2500 4898
 
1.5%
4000 3733
 
1.1%
3500 3683
 
1.1%
100 3123
 
0.9%
Other values (2184) 143222
43.2%
(Missing) 116364
35.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
35 1
 
< 0.1%
50 42
 
< 0.1%
100 3123
0.9%
115 1
 
< 0.1%
125 1
 
< 0.1%
130 1
 
< 0.1%
150 271
 
0.1%
192 1
 
< 0.1%
199 3
 
< 0.1%
ValueCountFrequency (%)
290500 1
< 0.1%
235500 1
< 0.1%
198000 1
< 0.1%
197400 1
< 0.1%
168000 1
< 0.1%
160500 1
< 0.1%
149700 1
< 0.1%
148400 1
< 0.1%
142415 1
< 0.1%
141500 1
< 0.1%

FREQUENCE_RECH
Real number (ℝ)

High correlation  Missing 

Distinct109
Distinct (%)0.1%
Missing116364
Missing (%)35.1%
Infinite0
Infinite (%)0.0%
Mean11.570923
Minimum1
Maximum133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-04-28T17:38:06.097832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q316
95-th percentile40
Maximum133
Range132
Interquartile range (IQR)14

Descriptive statistics

Standard deviation13.297436
Coefficient of variation (CV)1.1492114
Kurtosis5.2805668
Mean11.570923
Median Absolute Deviation (MAD)5
Skewness2.1059724
Sum2486626
Variance176.82181
MonotonicityNot monotonic
2025-04-28T17:38:06.384247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 33738
 
10.2%
2 21112
 
6.4%
3 17007
 
5.1%
4 13724
 
4.1%
5 11472
 
3.5%
6 9887
 
3.0%
7 8573
 
2.6%
8 7601
 
2.3%
9 6859
 
2.1%
10 6282
 
1.9%
Other values (99) 78648
23.7%
(Missing) 116364
35.1%
ValueCountFrequency (%)
1 33738
10.2%
2 21112
6.4%
3 17007
5.1%
4 13724
4.1%
5 11472
 
3.5%
6 9887
 
3.0%
7 8573
 
2.6%
8 7601
 
2.3%
9 6859
 
2.1%
10 6282
 
1.9%
ValueCountFrequency (%)
133 1
< 0.1%
117 1
< 0.1%
115 1
< 0.1%
113 1
< 0.1%
110 1
< 0.1%
108 2
< 0.1%
106 2
< 0.1%
105 1
< 0.1%
103 1
< 0.1%
101 2
< 0.1%

REVENUE
Real number (ℝ)

High correlation  Missing 

Distinct22391
Distinct (%)10.2%
Missing111611
Missing (%)33.7%
Infinite0
Infinite (%)0.0%
Mean5524.7335
Minimum1
Maximum221999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-04-28T17:38:06.628029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile199
Q11000
median3001
Q37400
95-th percentile18750.25
Maximum221999
Range221998
Interquartile range (IQR)6400

Descriptive statistics

Standard deviation7139.7239
Coefficient of variation (CV)1.2923201
Kurtosis26.041386
Mean5524.7335
Median Absolute Deviation (MAD)2499
Skewness3.484944
Sum1.2135409 × 109
Variance50975657
MonotonicityNot monotonic
2025-04-28T17:38:06.889560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 8957
 
2.7%
1000 5539
 
1.7%
1500 3177
 
1.0%
200 3034
 
0.9%
2000 2832
 
0.9%
3000 2041
 
0.6%
2500 1922
 
0.6%
3500 1371
 
0.4%
4000 1310
 
0.4%
100 1257
 
0.4%
Other values (22381) 188216
56.8%
(Missing) 111611
33.7%
ValueCountFrequency (%)
1 678
0.2%
2 497
0.2%
3 26
 
< 0.1%
4 303
0.1%
5 12
 
< 0.1%
6 160
 
< 0.1%
7 80
 
< 0.1%
8 199
 
0.1%
9 187
 
0.1%
10 421
0.1%
ValueCountFrequency (%)
221999 1
< 0.1%
162500 1
< 0.1%
147739 1
< 0.1%
146660 1
< 0.1%
144500 1
< 0.1%
132900 1
< 0.1%
129082 1
< 0.1%
126314 1
< 0.1%
124000 1
< 0.1%
121998 1
< 0.1%

ARPU_SEGMENT
Real number (ℝ)

High correlation  Missing 

Distinct10555
Distinct (%)4.8%
Missing111611
Missing (%)33.7%
Infinite0
Infinite (%)0.0%
Mean1841.5839
Minimum0
Maximum74000
Zeros678
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-04-28T17:38:07.173194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile66
Q1333
median1000
Q32467
95-th percentile6250
Maximum74000
Range74000
Interquartile range (IQR)2134

Descriptive statistics

Standard deviation2379.9044
Coefficient of variation (CV)1.2923138
Kurtosis26.041631
Mean1841.5839
Median Absolute Deviation (MAD)833
Skewness3.484959
Sum4.0451496 × 108
Variance5663945
MonotonicityNot monotonic
2025-04-28T17:38:07.445347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
167 10339
 
3.1%
333 6660
 
2.0%
500 4356
 
1.3%
667 3519
 
1.1%
67 3479
 
1.1%
1000 2864
 
0.9%
833 2394
 
0.7%
1167 1791
 
0.5%
1333 1685
 
0.5%
33 1656
 
0.5%
Other values (10545) 180913
54.6%
(Missing) 111611
33.7%
ValueCountFrequency (%)
0 678
0.2%
1 826
0.2%
2 252
 
0.1%
3 807
0.2%
4 437
0.1%
5 275
 
0.1%
6 187
 
0.1%
7 550
0.2%
8 117
 
< 0.1%
9 178
 
0.1%
ValueCountFrequency (%)
74000 1
< 0.1%
54167 1
< 0.1%
49246 1
< 0.1%
48887 1
< 0.1%
48167 1
< 0.1%
44300 1
< 0.1%
43027 1
< 0.1%
42105 1
< 0.1%
41333 1
< 0.1%
40666 1
< 0.1%

FREQUENCE
Real number (ℝ)

High correlation  Missing 

Distinct91
Distinct (%)< 0.1%
Missing111611
Missing (%)33.7%
Infinite0
Infinite (%)0.0%
Mean14.007571
Minimum1
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-04-28T17:38:07.633857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median9
Q320
95-th percentile45
Maximum91
Range90
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.712164
Coefficient of variation (CV)1.0503009
Kurtosis3.4119982
Mean14.007571
Median Absolute Deviation (MAD)7
Skewness1.7755519
Sum3076847
Variance216.44778
MonotonicityNot monotonic
2025-04-28T17:38:07.784649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 24893
 
7.5%
2 17668
 
5.3%
3 14608
 
4.4%
4 12744
 
3.8%
5 11037
 
3.3%
6 9836
 
3.0%
7 8866
 
2.7%
8 7945
 
2.4%
9 7360
 
2.2%
10 6655
 
2.0%
Other values (81) 98044
29.6%
(Missing) 111611
33.7%
ValueCountFrequency (%)
1 24893
7.5%
2 17668
5.3%
3 14608
4.4%
4 12744
3.8%
5 11037
3.3%
6 9836
 
3.0%
7 8866
 
2.7%
8 7945
 
2.4%
9 7360
 
2.2%
10 6655
 
2.0%
ValueCountFrequency (%)
91 14
 
< 0.1%
90 12
 
< 0.1%
89 17
 
< 0.1%
88 30
< 0.1%
87 42
< 0.1%
86 41
< 0.1%
85 38
< 0.1%
84 44
< 0.1%
83 60
< 0.1%
82 67
< 0.1%

DATA_VOLUME
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct20477
Distinct (%)12.2%
Missing163104
Missing (%)49.2%
Infinite0
Infinite (%)0.0%
Mean3378.6694
Minimum0
Maximum926547
Zeros49051
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-04-28T17:38:07.951081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median266
Q32913
95-th percentile15084.9
Maximum926547
Range926547
Interquartile range (IQR)2913

Descriptive statistics

Standard deviation12446.492
Coefficient of variation (CV)3.6838441
Kurtosis1379.4652
Mean3378.6694
Median Absolute Deviation (MAD)266
Skewness28.114949
Sum5.6816718 × 108
Variance1.5491515 × 108
MonotonicityNot monotonic
2025-04-28T17:38:08.121543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49051
 
14.8%
1 6286
 
1.9%
2 2079
 
0.6%
3 1069
 
0.3%
4 870
 
0.3%
1024 863
 
0.3%
5 720
 
0.2%
1023 608
 
0.2%
6 573
 
0.2%
7 496
 
0.1%
Other values (20467) 105548
31.9%
(Missing) 163104
49.2%
ValueCountFrequency (%)
0 49051
14.8%
1 6286
 
1.9%
2 2079
 
0.6%
3 1069
 
0.3%
4 870
 
0.3%
5 720
 
0.2%
6 573
 
0.2%
7 496
 
0.1%
8 468
 
0.1%
9 419
 
0.1%
ValueCountFrequency (%)
926547 1
< 0.1%
885642 1
< 0.1%
880424 1
< 0.1%
867127 1
< 0.1%
758167 1
< 0.1%
752018 1
< 0.1%
720309 1
< 0.1%
693842 1
< 0.1%
636022 1
< 0.1%
623124 1
< 0.1%

ON_NET
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct5587
Distinct (%)2.7%
Missing121131
Missing (%)36.6%
Infinite0
Infinite (%)0.0%
Mean278.62195
Minimum0
Maximum50809
Zeros16552
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-04-28T17:38:08.269437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median27
Q3158
95-th percentile1359
Maximum50809
Range50809
Interquartile range (IQR)153

Descriptive statistics

Standard deviation876.93852
Coefficient of variation (CV)3.1474136
Kurtosis151.48689
Mean278.62195
Median Absolute Deviation (MAD)26
Skewness8.6456016
Sum58548503
Variance769021.16
MonotonicityNot monotonic
2025-04-28T17:38:08.444083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16552
 
5.0%
1 14172
 
4.3%
2 8833
 
2.7%
3 6486
 
2.0%
7 6310
 
1.9%
8 6012
 
1.8%
4 5868
 
1.8%
5 4558
 
1.4%
6 4501
 
1.4%
9 3013
 
0.9%
Other values (5577) 133831
40.4%
(Missing) 121131
36.6%
ValueCountFrequency (%)
0 16552
5.0%
1 14172
4.3%
2 8833
2.7%
3 6486
 
2.0%
4 5868
 
1.8%
5 4558
 
1.4%
6 4501
 
1.4%
7 6310
 
1.9%
8 6012
 
1.8%
9 3013
 
0.9%
ValueCountFrequency (%)
50809 1
< 0.1%
30425 1
< 0.1%
25263 1
< 0.1%
25225 1
< 0.1%
24482 1
< 0.1%
24293 1
< 0.1%
24281 1
< 0.1%
23804 1
< 0.1%
23595 1
< 0.1%
21930 1
< 0.1%

ORANGE
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct1967
Distinct (%)1.0%
Missing137819
Missing (%)41.6%
Infinite0
Infinite (%)0.0%
Mean96.072397
Minimum0
Maximum6429
Zeros9496
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-04-28T17:38:08.600796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median29
Q399
95-th percentile396
Maximum6429
Range6429
Interquartile range (IQR)92

Descriptive statistics

Standard deviation206.70985
Coefficient of variation (CV)2.151605
Kurtosis94.783421
Mean96.072397
Median Absolute Deviation (MAD)27
Skewness7.3046543
Sum18585013
Variance42728.961
MonotonicityNot monotonic
2025-04-28T17:38:08.761958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10526
 
3.2%
0 9496
 
2.9%
2 7554
 
2.3%
3 5572
 
1.7%
4 5158
 
1.6%
8 3962
 
1.2%
5 3801
 
1.1%
6 3380
 
1.0%
10 3197
 
1.0%
7 3175
 
1.0%
Other values (1957) 137627
41.5%
(Missing) 137819
41.6%
ValueCountFrequency (%)
0 9496
2.9%
1 10526
3.2%
2 7554
2.3%
3 5572
1.7%
4 5158
1.6%
5 3801
 
1.1%
6 3380
 
1.0%
7 3175
 
1.0%
8 3962
 
1.2%
9 3121
 
0.9%
ValueCountFrequency (%)
6429 1
< 0.1%
6211 1
< 0.1%
6005 1
< 0.1%
5841 1
< 0.1%
5592 1
< 0.1%
5429 1
< 0.1%
5250 1
< 0.1%
5074 1
< 0.1%
4889 1
< 0.1%
4747 1
< 0.1%

TIGO
Real number (ℝ)

Missing  Zeros 

Distinct755
Distinct (%)0.6%
Missing198144
Missing (%)59.8%
Infinite0
Infinite (%)0.0%
Mean23.101921
Minimum0
Maximum2899
Zeros14458
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-04-28T17:38:08.924230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q320
95-th percentile95
Maximum2899
Range2899
Interquartile range (IQR)18

Descriptive statistics

Standard deviation62.385394
Coefficient of variation (CV)2.7004419
Kurtosis282.43796
Mean23.101921
Median Absolute Deviation (MAD)5
Skewness12.004446
Sum3075397
Variance3891.9374
MonotonicityNot monotonic
2025-04-28T17:38:09.075649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 17225
 
5.2%
0 14458
 
4.4%
2 11121
 
3.4%
3 8227
 
2.5%
4 6599
 
2.0%
5 5351
 
1.6%
6 4612
 
1.4%
7 3979
 
1.2%
8 3821
 
1.2%
9 3267
 
1.0%
Other values (745) 54463
 
16.4%
(Missing) 198144
59.8%
ValueCountFrequency (%)
0 14458
4.4%
1 17225
5.2%
2 11121
3.4%
3 8227
2.5%
4 6599
 
2.0%
5 5351
 
1.6%
6 4612
 
1.4%
7 3979
 
1.2%
8 3821
 
1.2%
9 3267
 
1.0%
ValueCountFrequency (%)
2899 1
< 0.1%
2758 1
< 0.1%
2663 1
< 0.1%
2625 1
< 0.1%
2568 1
< 0.1%
2554 1
< 0.1%
1896 1
< 0.1%
1756 1
< 0.1%
1732 1
< 0.1%
1694 1
< 0.1%

ZONE1
Real number (ℝ)

Missing  Zeros 

Distinct319
Distinct (%)1.2%
Missing305097
Missing (%)92.1%
Infinite0
Infinite (%)0.0%
Mean8.1801681
Minimum0
Maximum1867
Zeros9212
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-04-28T17:38:09.230227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile32
Maximum1867
Range1867
Interquartile range (IQR)4

Descriptive statistics

Standard deviation40.985521
Coefficient of variation (CV)5.010352
Kurtosis598.01951
Mean8.1801681
Median Absolute Deviation (MAD)1
Skewness19.624853
Sum214075
Variance1679.813
MonotonicityNot monotonic
2025-04-28T17:38:09.433764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9212
 
2.8%
1 6456
 
1.9%
2 2544
 
0.8%
3 1397
 
0.4%
4 938
 
0.3%
5 659
 
0.2%
6 511
 
0.2%
7 392
 
0.1%
8 337
 
0.1%
9 315
 
0.1%
Other values (309) 3409
 
1.0%
(Missing) 305097
92.1%
ValueCountFrequency (%)
0 9212
2.8%
1 6456
1.9%
2 2544
 
0.8%
3 1397
 
0.4%
4 938
 
0.3%
5 659
 
0.2%
6 511
 
0.2%
7 392
 
0.1%
8 337
 
0.1%
9 315
 
0.1%
ValueCountFrequency (%)
1867 1
< 0.1%
1609 1
< 0.1%
1529 1
< 0.1%
1469 1
< 0.1%
1427 1
< 0.1%
1360 1
< 0.1%
1220 1
< 0.1%
963 1
< 0.1%
883 1
< 0.1%
865 1
< 0.1%

ZONE2
Real number (ℝ)

Missing  Zeros 

Distinct236
Distinct (%)1.1%
Missing310242
Missing (%)93.7%
Infinite0
Infinite (%)0.0%
Mean7.2950297
Minimum0
Maximum1346
Zeros6217
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-04-28T17:38:09.627924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile27
Maximum1346
Range1346
Interquartile range (IQR)5

Descriptive statistics

Standard deviation30.213219
Coefficient of variation (CV)4.141617
Kurtosis567.06608
Mean7.2950297
Median Absolute Deviation (MAD)2
Skewness19.146683
Sum153378
Variance912.83861
MonotonicityNot monotonic
2025-04-28T17:38:09.788163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6217
 
1.9%
1 4132
 
1.2%
2 2340
 
0.7%
3 1502
 
0.5%
4 1140
 
0.3%
5 793
 
0.2%
6 561
 
0.2%
7 497
 
0.2%
8 380
 
0.1%
9 320
 
0.1%
Other values (226) 3143
 
0.9%
(Missing) 310242
93.7%
ValueCountFrequency (%)
0 6217
1.9%
1 4132
1.2%
2 2340
 
0.7%
3 1502
 
0.5%
4 1140
 
0.3%
5 793
 
0.2%
6 561
 
0.2%
7 497
 
0.2%
8 380
 
0.1%
9 320
 
0.1%
ValueCountFrequency (%)
1346 1
< 0.1%
1174 1
< 0.1%
1039 1
< 0.1%
1007 1
< 0.1%
932 1
< 0.1%
799 1
< 0.1%
702 1
< 0.1%
660 1
< 0.1%
639 1
< 0.1%
608 1
< 0.1%

MRG
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size323.6 KiB
False
331267 
ValueCountFrequency (%)
False 331267
100.0%
2025-04-28T17:38:09.872309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

REGULARITY
Real number (ℝ)

High correlation 

Distinct62
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.063873
Minimum1
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-04-28T17:38:09.972855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median24
Q351
95-th percentile62
Maximum62
Range61
Interquartile range (IQR)45

Descriptive statistics

Standard deviation22.288201
Coefficient of variation (CV)0.79419548
Kurtosis-1.4869465
Mean28.063873
Median Absolute Deviation (MAD)20
Skewness0.24551932
Sum9296635
Variance496.76391
MonotonicityNot monotonic
2025-04-28T17:38:10.127775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 30160
 
9.1%
62 25840
 
7.8%
2 18352
 
5.5%
3 13021
 
3.9%
4 10496
 
3.2%
61 9792
 
3.0%
5 8704
 
2.6%
6 7749
 
2.3%
60 7328
 
2.2%
7 6849
 
2.1%
Other values (52) 192976
58.3%
ValueCountFrequency (%)
1 30160
9.1%
2 18352
5.5%
3 13021
3.9%
4 10496
 
3.2%
5 8704
 
2.6%
6 7749
 
2.3%
7 6849
 
2.1%
8 6301
 
1.9%
9 5653
 
1.7%
10 5283
 
1.6%
ValueCountFrequency (%)
62 25840
7.8%
61 9792
 
3.0%
60 7328
 
2.2%
59 6182
 
1.9%
58 5288
 
1.6%
57 4814
 
1.5%
56 4445
 
1.3%
55 4205
 
1.3%
54 4067
 
1.2%
53 3836
 
1.2%

TOP_PACK
Text

Missing 

Distinct108
Distinct (%)0.1%
Missing138593
Missing (%)41.8%
Memory size2.5 MiB
2025-04-28T17:38:10.371000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length42
Mean length23.186102
Min length2

Characters and Unicode

Total characters4467359
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowOn net 200F=Unlimited _call24H
2nd rowOn-net 1000F=10MilF;10d
3rd rowData:1000F=5GB,7d
4th rowMixt 250F=Unlimited_call24H
5th rowMIXT:500F= 2500F on net _2500F off net;2d
ValueCountFrequency (%)
all-net 59457
 
12.4%
500f=2000f;5d 48735
 
10.1%
net 39624
 
8.2%
on 36664
 
7.6%
200f=unlimited 23422
 
4.9%
call24h 23422
 
4.9%
2500f 19978
 
4.2%
data 19690
 
4.1%
data:490f=1gb,7d 17794
 
3.7%
mixt 14245
 
3.0%
Other values (147) 177487
36.9%
2025-04-28T17:38:10.758197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 629738
 
14.1%
287844
 
6.4%
l 270658
 
6.1%
F 259719
 
5.8%
t 248873
 
5.6%
n 224266
 
5.0%
2 209273
 
4.7%
e 180460
 
4.0%
a 171720
 
3.8%
5 170184
 
3.8%
Other values (61) 1814624
40.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4467359
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 629738
 
14.1%
287844
 
6.4%
l 270658
 
6.1%
F 259719
 
5.8%
t 248873
 
5.6%
n 224266
 
5.0%
2 209273
 
4.7%
e 180460
 
4.0%
a 171720
 
3.8%
5 170184
 
3.8%
Other values (61) 1814624
40.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4467359
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 629738
 
14.1%
287844
 
6.4%
l 270658
 
6.1%
F 259719
 
5.8%
t 248873
 
5.6%
n 224266
 
5.0%
2 209273
 
4.7%
e 180460
 
4.0%
a 171720
 
3.8%
5 170184
 
3.8%
Other values (61) 1814624
40.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4467359
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 629738
 
14.1%
287844
 
6.4%
l 270658
 
6.1%
F 259719
 
5.8%
t 248873
 
5.6%
n 224266
 
5.0%
2 209273
 
4.7%
e 180460
 
4.0%
a 171720
 
3.8%
5 170184
 
3.8%
Other values (61) 1814624
40.6%

FREQ_TOP_PACK
Real number (ℝ)

High correlation  Missing 

Distinct165
Distinct (%)0.1%
Missing138594
Missing (%)41.8%
Infinite0
Infinite (%)0.0%
Mean9.2994504
Minimum1
Maximum592
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2025-04-28T17:38:10.905957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q312
95-th percentile33
Maximum592
Range591
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.301065
Coefficient of variation (CV)1.3227733
Kurtosis49.162582
Mean9.2994504
Median Absolute Deviation (MAD)4
Skewness3.9116439
Sum1791753
Variance151.3162
MonotonicityNot monotonic
2025-04-28T17:38:11.057720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 38577
 
11.6%
2 23737
 
7.2%
3 18152
 
5.5%
4 13151
 
4.0%
5 10468
 
3.2%
6 8813
 
2.7%
7 7637
 
2.3%
8 6734
 
2.0%
9 5916
 
1.8%
10 5350
 
1.6%
Other values (155) 54138
 
16.3%
(Missing) 138594
41.8%
ValueCountFrequency (%)
1 38577
11.6%
2 23737
7.2%
3 18152
5.5%
4 13151
 
4.0%
5 10468
 
3.2%
6 8813
 
2.7%
7 7637
 
2.3%
8 6734
 
2.0%
9 5916
 
1.8%
10 5350
 
1.6%
ValueCountFrequency (%)
592 1
< 0.1%
316 1
< 0.1%
308 1
< 0.1%
304 1
< 0.1%
294 1
< 0.1%
262 1
< 0.1%
217 1
< 0.1%
215 1
< 0.1%
214 1
< 0.1%
206 1
< 0.1%

CHURN
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size2.5 MiB
0.0
269268 
1.0
61998 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters993798
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 269268
81.3%
1.0 61998
 
18.7%
(Missing) 1
 
< 0.1%

Length

2025-04-28T17:38:11.193546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-28T17:38:11.264879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 269268
81.3%
1.0 61998
 
18.7%

Most occurring characters

ValueCountFrequency (%)
0 600534
60.4%
. 331266
33.3%
1 61998
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 993798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 600534
60.4%
. 331266
33.3%
1 61998
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 993798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 600534
60.4%
. 331266
33.3%
1 61998
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 993798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 600534
60.4%
. 331266
33.3%
1 61998
 
6.2%

Interactions

2025-04-28T17:37:57.378870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:32.597855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:34.492108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:36.391808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:38.453876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:41.441596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:43.207478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:45.223727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:47.028618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:48.834445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:50.627217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:53.394163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:55.466377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:57.526287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:32.758855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:34.658173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:36.519195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:38.658386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:41.586334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:43.333831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:45.376315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:47.165743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:48.967964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:50.757334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:53.599018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:55.613142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:57.664744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:32.890271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:34.800903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:36.669664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:38.865902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:41.734850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:43.460734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:45.547451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:47.327681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:49.099007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:50.889866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:53.806211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:55.761582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:57.802933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:33.049692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:34.944704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:36.812298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:39.072069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:41.868078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:43.580498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:45.672137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:47.460942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:49.230004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:51.521057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:53.985297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:55.919272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:57.958207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:33.203362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:35.097271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:36.970242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:39.305944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:42.017030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:43.716737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:45.819392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:47.616332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:49.409958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:51.723778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:54.197421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:56.064384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:58.102926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:33.344737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:35.231234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:37.098329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:39.807897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:42.152957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:43.839216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:45.946863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:47.733972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:49.531472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:51.890958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:54.392271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:56.186277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:58.234240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:33.490586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:35.375410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:37.251870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:40.036434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:42.279595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:43.973554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:46.107317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:47.872688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:49.679775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:52.066757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:54.533175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:56.336866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:58.378457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:33.649788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:35.521964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:37.379679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:40.272146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:42.416152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:44.111764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:46.252533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:48.015458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:49.810972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:52.239887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:54.657561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:56.486175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:58.502356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:33.803297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:35.670260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:37.504940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:40.489435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:42.544022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:44.253426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:46.389629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:48.137908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:49.946936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:52.420026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:54.799325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:56.626939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:58.629434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:33.913337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:35.800557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:37.617933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:40.703861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:42.658329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:44.388722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:46.504102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:48.265516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:50.068929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:52.626830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:54.914428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:56.756177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:58.751487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:34.026297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:35.927288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:37.822437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:40.956066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:42.774628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:44.516234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:46.625458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:48.405462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:50.190857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:52.796144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:55.045869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:56.905692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:58.903031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:34.185718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:36.093066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:38.034546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:41.131591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:42.923689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:44.671423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:46.761838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:48.549541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:50.350724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:52.980598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:55.173467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:57.092088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:59.062992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:34.343998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:36.238594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:38.243235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:41.279175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:43.052094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:44.786943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:46.891502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:48.685556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:50.497201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:53.179160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:55.297972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-28T17:37:57.233883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-28T17:38:11.356100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ARPU_SEGMENTCHURNDATA_VOLUMEFREQUENCEFREQUENCE_RECHFREQ_TOP_PACKMONTANTON_NETORANGEREGIONREGULARITYREVENUETENURETIGOZONE1ZONE2
ARPU_SEGMENT1.0000.0360.3900.8810.8800.8160.9870.5220.6780.0280.7161.0000.0140.4510.2160.311
CHURN0.0361.0000.0040.1470.1080.0170.0230.0150.0240.0320.5550.0360.0510.0070.0000.026
DATA_VOLUME0.3900.0041.0000.3310.2960.2290.380-0.095-0.0190.0150.3060.3900.030-0.010-0.024-0.002
FREQUENCE0.8810.1470.3311.0000.9520.8670.8710.4410.5300.0540.6910.8810.0040.3360.0790.188
FREQUENCE_RECH0.8800.1080.2960.9521.0000.8950.8870.4780.5630.0470.6780.8800.0000.3640.0860.181
FREQ_TOP_PACK0.8160.0170.2290.8670.8951.0000.8110.4390.5380.0160.5960.8170.0010.3510.0930.062
MONTANT0.9870.0230.3800.8710.8870.8111.0000.5120.6690.0210.7080.9870.0110.4480.2130.310
ON_NET0.5220.015-0.0950.4410.4780.4390.5121.0000.5500.0090.5250.5220.0000.3700.064-0.022
ORANGE0.6780.024-0.0190.5300.5630.5380.6690.5501.0000.0160.4560.6780.0140.4700.1200.052
REGION0.0280.0320.0150.0540.0470.0160.0210.0090.0161.0000.0370.0280.0240.0120.0000.000
REGULARITY0.7160.5550.3060.6910.6780.5960.7080.5250.4560.0371.0000.7160.0170.3230.0470.044
REVENUE1.0000.0360.3900.8810.8800.8170.9870.5220.6780.0280.7161.0000.0140.4510.2150.311
TENURE0.0140.0510.0300.0040.0000.0010.0110.0000.0140.0240.0170.0141.0000.0000.0220.072
TIGO0.4510.007-0.0100.3360.3640.3510.4480.3700.4700.0120.3230.4510.0001.0000.0750.018
ZONE10.2160.000-0.0240.0790.0860.0930.2130.0640.1200.0000.0470.2150.0220.0751.0000.107
ZONE20.3110.026-0.0020.1880.1810.0620.310-0.0220.0520.0000.0440.3110.0720.0180.1071.000

Missing values

2025-04-28T17:37:59.763447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-28T17:38:00.505254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-28T17:38:02.839851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

user_idREGIONTENUREMONTANTFREQUENCE_RECHREVENUEARPU_SEGMENTFREQUENCEDATA_VOLUMEON_NETORANGETIGOZONE1ZONE2MRGREGULARITYTOP_PACKFREQ_TOP_PACKCHURN
000000bfd7d50f01092811bc0c8d7b0d6fe7c3596FATICKK > 24 month4250.015.04251.01417.017.04.0388.046.01.01.02.0NO54On net 200F=Unlimited _call24H8.00.0
100000cb4a5d760de88fecb38e2f71b7bec52e834NaNI 18-21 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO4NaNNaN1.0
200001654a9d9f96303d9969d0a4a851714a4bb57NaNK > 24 month3600.02.01020.0340.02.0NaN90.046.07.0NaNNaNNO17On-net 1000F=10MilF;10d1.00.0
300001dd6fa45f7ba044bd5d84937be464ce78ac2DAKARK > 24 month13500.015.013502.04501.018.043804.041.0102.02.0NaNNaNNO62Data:1000F=5GB,7d11.00.0
4000028d9e13a595abe061f9b58f3d76ab907850fDAKARK > 24 month1000.01.0985.0328.01.0NaN39.024.0NaNNaNNaNNO11Mixt 250F=Unlimited_call24H2.00.0
50000296564272665ccd2925d377e124f3306b01eLOUGAK > 24 month8500.017.09000.03000.018.0NaN252.070.091.0NaNNaNNO62MIXT:500F= 2500F on net _2500F off net;2d18.00.0
600002b0ed56e2c199ec8c3021327229afa70f063LOUGAK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO2NaNNaN0.0
70000313946b6849745963442c6e572d47cd24cedDAKARK > 24 month7000.016.07229.02410.022.01601.077.029.0100.0NaNNaNNO55All-net 500F=2000F;5d8.00.0
80000398021ccd3a488fa1a63dee3b2f0d471f9fdDAKARK > 24 month1500.03.01502.0501.012.0NaN2.053.02.0NaNNaNNO31NaNNaN0.0
900003d165737109921ebd21f883cb8cff028b626TAMBACOUNDAK > 24 month4000.08.04000.01333.08.0NaN1620.09.0NaNNaNNaNNO45On-net 500F_FNF;3d8.00.0
user_idREGIONTENUREMONTANTFREQUENCE_RECHREVENUEARPU_SEGMENTFREQUENCEDATA_VOLUMEON_NETORANGETIGOZONE1ZONE2MRGREGULARITYTOP_PACKFREQ_TOP_PACKCHURN
331257276e5b3ea0f146740ea1df8a4c349c14d9e17f69NaNK > 24 month500.01.0500.0167.01.0NaN8.0NaNNaNNaNNaNNO12On-net 500=4000,10d1.00.0
331258276e6110fc32f41b35088354be98dafabd50e368LOUGAK > 24 month3000.03.02987.0996.07.0NaN16.015.02.03.0NaNNO21NaNNaN0.0
331259276e68d1807217b3e7876e6fcd0cc540a8972bddDAKARK > 24 month3000.04.02999.01000.07.06763.04.038.01.0NaNNaNNO54Data:1000F=2GB,30d3.00.0
331260276e69675e2cf6b3999695469721f854a1727361DAKARK > 24 month3500.06.03499.01166.08.0NaN51.074.0NaNNaNNaNNO33All-net 500F=2000F;5d3.00.0
331261276e72a09dd82f06403fa653a92a3da7d5da767bNaNK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO1NaNNaN1.0
331262276e80e1438ceef01c586fbced93ad064e2ea4fbNaNK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO1NaNNaN0.0
331263276e81372ba8b1fa4092dd0f986af500e8724667NaNK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO1NaNNaN0.0
331264276e862452e4f2c28a5a56b48ad17abe41ed553eNaNK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO10NaNNaN1.0
331265276e89e10fb5d85cd567e8d50a3f2bbdfbd10eb9DAKARK > 24 month500.01.0502.0167.02.0NaN46.018.0NaNNaNNaNNO6All-net 500F=2000F;5d1.00.0
331266276e99744062bb493e52ef26a09e1dfc5446cea6KAFFRINEK > 24 month3500.07.03500.01167.07.0NaN58.088.02.0NaNNaNNO48MINaNNaN